The present invention relates to a postprocessing technique of medical images, and more particularly to the technique of extracting desired regions, such as lesions and organs, as ROIs (regions of interest) from tissues of an object imaged.
In acquiring valuable information on medical activities today, it is very useful to collect and analyze image data within a ROI designated on a plane image provided by various medical image diagnostic systems.
Image data from a ROI was mainly collected with a threshold value technique in the past. However, the threshold value technique referred to only the comparison between all pixel intensities and the threshold value, thus sometimes misleading to collection of image data in the outside of the ROI.
To avoid the above drawback, another technique utilizing histograms has now been in general use.
In the histogram technique for MRI(magnetic resonance technique), for example, a matched pair of images sliced at an identical slicing position of a patient is prepared. The pair of images is given under different imaging conditions or different calculation processes for raw data, and are, for instance, T1- and T2-weighted images A and B in MRI. The prepared two slicing image data are each processed with an individual threshold range to produce two individual histogram data. As a result, two extracted regions E.sub.A and E.sub.B come out and are then processed by an AND(conjunction) operation or an OR(disjunction) operation to produce a final ROI. The ROI finally produced represents a correlation of configurations between the two extracted regions E.sub.A and E.sub.B according to the two threshold ranges.
By the way, it has been suggested that images collected from a living body include not only configurative information of its tissues but functional information of them. Based on this suggestion, much research has been carried out and reference is made to the following articles:
1) ANDREAS HERRMANN, DAVID N. LEVIN, and ROBERT N. BECK, "Oscillating Intensity Display of Soft Tissue Lesions in MRI", IEEE Transactions on Medical Imaging, Vol. MI-6, No. 4, 370-373, 1987; and
2) MICHAEL JUST and MANFRED THELEN, "Tissue Characterization with T1, T2, and Proton Density Values: Results in 160 Patients with Brain Tumors", Radiology, 169, 779-785, 1988.
HERRMANN et al. discloses a technique by which soft tissues lesions can be estimated according to correlation of pixel intensities between T1- and T2-weighted images by MRI on a two-dimensional histogram (intensity plane: see FIG. 2 on page 371). Also disclosed by JUST is a technique by which tissue characterization can be made with correlation of pixel intensities among T1- and T2-weighted images and proton density images.
When reducing the results of the research to practice, it is required to delineate a ROI on a monitor. In such a case, it is impossible to utilize the above-described ROI technique for highly accurate delineation, because the conventional technique devotes its whole attention to configurative correlation between the images A and B determined by theshold ranges.